Biblio

In recent years, attacks against cyber-physical systems have become increasingly frequent and widespread. The inventiveness of such attacks increases significantly. In particular, zero-day attacks are widely used. The rapid development of the industrial Internet of things, the expansion of the application areas of service robots, the advent of the Internet of vehicles and the Internet of military things have led to a significant increase of attention to deceptive attacks. Especially great threat is posed by deceptive attacks that do not use hiding malicious components. Such attacks can naturally be used against robotic systems. In this paper, we consider an approach to the development of an intrusion detection system for closed-loop robotic systems. The system is based on an abnormal behavioral pattern detection technique. The system can be used for detection of zero-day deceptive attacks. We provide an experimental comparison of our approach and other behavior-based intrusion detection systems.

In the paper, our research of abnormal bus data analysis of intelligent and connected vehicle aims to detect the abnormal data rapidly and accurately generated by the hackers who send malicious commands to attack vehicles through three patterns, including remote non-contact, short-range non-contact and contact. The research routine is as follows: Take the bus data of 10 different brands of intelligent and connected vehicles through the real vehicle experiments as the research foundation, set up the optimized neural network, collect 1000 sets of the normal bus data of 15 kinds of driving scenarios and the other 300 groups covering the abnormal bus data generated by attacking the three systems which are most common in the intelligent and connected vehicles as the training set. In the end after repeated amendments, with 0.5 seconds per detection, the intrusion detection system has been attained in which for the controlling system the abnormal bus data is detected at the accuracy rate of 96% and the normal data is detected at the accuracy rate of 90%, for the body system the abnormal one is 87% and the normal one is 80%, for the entertainment system the abnormal one is 80% and the normal one is 65%.

Intrusion detection systems installed on the information security devices that control the internal and external perimeter of the demilitarized zones are not able to detect the vulnerability of ZeroLogon after the successful penetration of the intruder into the zone. Component solution for ZeroLogon control is offered. The paper presents the research results of the capabilities for built-in Active Directory audit mechanisms and open source intrusion detection/prevention systems, which allow identification of the critical vulnerability CVE-2020-1472. These features can be used to improve the quality of cyber-physical systems management, to perform audits, as well as to check corporate domains for ZeroLogon vulnerabilities.

Active defense is a popular defense technique based on systems that hinder an attacker's progress by design, rather than reactively responding to an attack only after its detection. Well-known active defense systems are honeypots. Honeypots are fake systems, designed to look like real production systems, aimed at trapping an attacker, and analyzing his attack strategy and goals. These types of systems suffer from a major weakness: it is extremely hard to design them in such a way that an attacker cannot distinguish them from a real production system. In this paper, we advocate that, instead of adding additional fake systems in the corporate network, the production systems themselves should be instrumented to provide active defense capabilities. This perspective to active defense allows containing costs and complexity, while at the same time provides the attacker with a more realistic-looking target, and gives the Incident Response Team more time to identify the attacker. The proposed proof-of-concept prototype system can be used to implement active defense in any corporate production network, with little upfront work, and little maintenance.

With the booming of Internet technology, the continuous emergence of new technologies and new algorithms greatly expands the application boundaries of cyberspace. While enjoying the convenience brought by informatization, the society is also facing increasingly severe threats to the security of cyberspace. In cyber security defense, cyberspace operators rely on the discovered vulnerabilities, attack patterns, TTPs, and other knowledge to observe, analyze and determine the current threats to the network and security situation in cyberspace, and then make corresponding decisions. However, most of such open-source knowledge is distributed in different data sources in the form of text or web pages, which is not conducive to the understanding, query and correlation analysis of cyberspace operators. In this paper, a knowledge graph for cyber security is constructed to solve this problem. At first, in the process of obtaining security data from multi-source heterogeneous cyberspaces, we adopt efficient crawler to crawl the required data, paving the way for knowledge graph building. In order to establish the ontology required by the knowledge graph, we abstract the overall framework of security data sources in cyberspace, and depict in detail the correlations among various data sources. Then, based on the \$$\backslash$mathbfOWL +$\backslash$mathbfSWRL\$ language, we construct the cyber security knowledge graph. On this basis, we design an analysis system for situation in cyberspace based on knowledge graph and the Snort intrusion detection system (IDS), and study the rules in Snort. The system integrates and links various public resources from the Internet, including key information such as general platforms, vulnerabilities, weaknesses, attack patterns, tactics, techniques, etc. in real cyberspace, enabling the provision of comprehensive, systematic and rich cyber security knowledge to security researchers and professionals, with the expectation to provide a useful reference for cyber security defense.

In this cyber era, the number of cybercrime problems grows significantly, impacting network communication security. Some factors have been identified, such as malware. It is a malicious code attack that is harmful. On the other hand, a botnet can exploit malware to threaten whole computer networks. Therefore, it needs to be handled appropriately. Several botnet activity detection models have been developed using a classification approach in previous studies. However, it has not been analyzed about selecting features to be used in the learning process of the classification algorithm. In fact, the number and selection of features implemented can affect the detection accuracy of the classification algorithm. This paper proposes an analysis technique for determining the number and selection of features developed based on previous research. It aims to obtain the analysis of using features. The experiment has been conducted using several classification algorithms, namely Decision tree, k-NN, Naïve Bayes, Random Forest, and Support Vector Machine (SVM). The results show that taking a certain number of features increases the detection accuracy. Compared with previous studies, the results obtained show that the average detection accuracy of 98.34% using four features has the highest value from the previous study, 97.46% using 11 features. These results indicate that the selection of the correct number and features affects the performance of the botnet detection model.

The domain of securing in-vehicle networks has attracted both academic and industrial researchers due to high danger of attacks on drivers and passengers. While securing wired and wireless interfaces is important to defend against these threats, detecting attacks is still the critical phase to construct a robust secure system. There are only a few results on securing communication inside vehicles using anomaly-detection techniques despite their efficiencies in systems that need real-time detection. Therefore, we propose an intrusion detection system (IDS) based on Multi-Layer Perceptron (MLP) neural network for Controller Area Networks (CAN) bus. This IDS divides data according to the ID field of CAN packets using K-means clustering algorithm, then it extracts suitable features and uses them to train and construct the neural network. The proposed IDS works for each ID separately and finally it combines their individual decisions to construct the final score and generates alert in the presence of attack. The strength of our intrusion detection method is that it works simultaneously for two types of attacks which will eliminate the use of several separate IDS and thus reduce the complexity and cost of implementation.

Cyber-attacks are becoming more complex & increasing tasks in accurate intrusion detection (ID). Failure to avoid intrusion can reduce the reliability of security services, for example, integrity, Privacy & availability of data. The rapid proliferation of computer networks (CNs) has reformed the perception of network security. Easily accessible circumstances affect computer networks from many threats by hackers. Threats to a network are many & hypothetically devastating. Researchers have recognized an Intrusion Detection System (IDS) up to identifying attacks into a wide variety of environments. Several approaches to intrusion detection, usually identified as Signature-based Intrusion Detection Systems (SIDS) & Anomaly-based Intrusion Detection Systems (AIDS), were proposed in the literature to address computer safety hazards. This survey paper grants a review of current IDS, complete analysis of prominent new works & generally utilized dataset to evaluation determinations. It also introduces avoidance techniques utilized by attackers to avoid detection. This paper delivers a description of AIDS for attack detection. IDS is an applied research area in artificial intelligence (AI) that uses multiple machine learning algorithms.

Recently, the Distributed Denial of Service (DDOS) attacks has been used for different aspects to denial the number of services for the end-users. Therefore, there is an urgent need to design an effective detection method against this type of attack. A fuzzy inference system offers the results in a more readable and understandable form. This paper introduces an anomaly-based Intrusion Detection (IDS) system using fuzzy logic. The fuzzy logic inference system implemented as a detection method for Distributed Denial of Service (DDOS) attacks. The suggested method was applied to an open-source DDOS dataset. Experimental results show that the anomaly-based Intrusion Detection system using fuzzy logic obtained the best result by utilizing the InfoGain features selection method besides the fuzzy inference system, the results were 91.1% for the true-positive rate and 0.006% for the false-positive rate.

In recent years, cyber-attacks on modern industrial control systems (ICS) have become more common and it acts as a victim to various kind of attackers. The percentage of attacked ICS computers in the world in 2021 is 39.6%. To identify the anomaly in a large database system is a challenging task. Deep-learning model provides better solutions for handling the huge dataset with good accuracy. On the other hand, real time datasets are highly imbalanced with their sample proportions. In this research, GAN based model, a supervised learning method which generates new fake samples that is similar to real samples has been proposed. GAN based adversarial training would address the class imbalance problem in real time datasets. Adversarial samples are combined with legitimate samples and shuffled via proper proportion and given as input to the classifiers. The generated data samples along with the original ones are classified using various machine learning classifiers and their performances have been evaluated. Gradient boosting was found to classify with 98% accuracy when compared to other

Most serious occurrence of modern malware is Botnet. Botnet is a rapidly evolving problem that is still not well understood and studied. One of the main goals for modern network security is to create adequate techniques for the detection and eventual termination of Botnet threats. The article presents an approach for implementing a host-based Intrusion Detection System for Botnet attack detection. The approach is based on a variation of a genetic algorithm to detect anomalies in a case of attacks. An implementation of the approach and experimental results are presented.

One of the latest emerging technologies is artificial intelligence, which makes the machine mimic human behavior. The most important component used to detect cyber attacks or malicious activities is the Intrusion Detection System (IDS). Artificial intelligence plays a vital role in detecting intrusions and widely considered as the better way in adapting and building IDS. In trendy days, artificial intelligence algorithms are rising as a brand new computing technique which will be applied to actual time issues. In modern days, neural network algorithms are emerging as a new artificial intelligence technique that can be applied to real-time problems. The proposed system is to detect a classification of botnet attack which poses a serious threat to financial sectors and banking services. The proposed system is created by applying artificial intelligence on a realistic cyber defense dataset (CSE-CIC-IDS2018), the very latest Intrusion Detection Dataset created in 2018 by Canadian Institute for Cybersecurity (CIC) on AWS (Amazon Web Services). The proposed system of Artificial Neural Networks provides an outstanding performance of Accuracy score is 99.97% and an average area under ROC (Receiver Operator Characteristic) curve is 0.999 and an average False Positive rate is a mere value of 0.001. The proposed system using artificial intelligence of botnet attack detection is powerful, more accurate and precise. The novel proposed system can be implemented in n machines to conventional network traffic analysis, cyber-physical system traffic data and also to the real-time network traffic analysis.

Nowadays, the networks attacks became very sophisticated and hard to be recognized, The traditional types of intrusion detection systems became inefficient in predicting new types of attacks. As the IDS is an important factor in securing the network in the real time, many new effective IDS approaches have been proposed. In this paper, we intend to discuss different Artificial Neural Networks based IDS approaches, also we are going to categorize them in four categories (normal ANN, DNN, CNN, RNN) and make a comparison between them depending on different performance parameters (accuracy, FNR, FPR, training time, epochs and the learning rate) and other factors like the network structure, the classification type, the used dataset. At the end of the survey, we will mention the merits and demerits of each approach and suggest some enhancements to avoid the noticed drawbacks.

Internet Engineering Task Force (IETF) is working on 6LoW-PAN standard which allows smart devices to be connected to Internet using large address space of IPV6. 6LoWPAN acts as a bridge between resource constrained devices and the Internet. The entire IoT space is vulnerable to local threats as well as the threats from the Internet. Due to the random deployment of the network nodes and the absence of tamper resistant shield, the resource constrained IoT elements face potential insider attacks even in presence of front line defense mechanism that involved cryptographic protocols. To detect such insidious nodes, an Intrusion Detection System (IDS) is required as a second line of defense. In this paper, we attempt to analyze such potential insider attacks, while reviewing the IDS based countermeasures. We attempt to propose a baseline for designing IDS for 6LoWPAN based IoT system.

Technological advancement enables the need of internet everywhere. The power industry is not an exception in the technological advancement which makes everything smarter. Smart grid is the advanced version of the traditional grid, which makes the system more efficient and self-healing. Synchrophasor is a device used in smart grids to measure the values of electric waves, voltages and current. The phasor measurement unit produces immense volume of current and voltage data that is used to monitor and control the performance of the grid. These data are huge in size and vulnerable to attacks. Intrusion Detection is a common technique for finding the intrusions in the system. In this paper, a big data framework is designed using various machine learning techniques, and intrusions are detected based on the classifications applied on the synchrophasor dataset. In this approach various machine learning techniques like deep neural networks, support vector machines, random forest, decision trees and naive bayes classifications are done for the synchrophasor dataset and the results are compared using metrics of accuracy, recall, false rate, specificity, and prediction time. Feature selection and dimensionality reduction algorithms are used to reduce the prediction time taken by the proposed approach. This paper uses apache spark as a platform which is suitable for the implementation of Intrusion Detection system in smart grids using big data analytics.

Due to Bitcoin's innovative block structure, it is both immutable and decentralized, making it a valuable tool or instrument for changing current financial systems. However, the appealing features of Bitcoin have also drawn the attention of cybercriminals. The Bitcoin scripting system allows users to include up to 80 bytes of arbitrary data in Bitcoin transactions, making it possible to store illegal information in the blockchain. This makes Bitcoin a powerful tool for obfuscating information and using it as the command-and-control infrastructure for blockchain-based botnets. On the other hand, Blockchain offers an intriguing solution for IoT security. Blockchain provides strong protection against data tampering, locks Internet of Things devices, and enables the shutdown of compromised devices within an IoT network. Thus, blockchain could be used both to attack and defend IoT networks and communications.

Despite the increased accuracy of intrusion detection systems (IDS) in identifying cyberattacks in computer networks and devices connected to the internet, distributed or coordinated attacks can still go undetected or not detected on time. The single vantage point limits the ability of these IDSs to detect such attacks. Due to this reason, there is a need for attack characteristics' exchange among different IDS nodes. Researchers proposed a cooperative intrusion detection system to share these attack characteristics effectively. This approach was useful; however, the security of the shared data cannot be guaranteed. More specifically, maintaining the integrity and consistency of shared data becomes a significant concern. In this paper, we propose a blockchain-based solution that ensures the integrity and consistency of attack characteristics shared in a cooperative intrusion detection system. The proposed architecture achieves this by detecting and preventing fake features injection and compromised IDS nodes. It also facilitates scalable attack features exchange among IDS nodes, ensures heterogeneous IDS nodes participation, and it is robust to public IDS nodes joining and leaving the network. We evaluate the security analysis and latency. The result shows that the proposed approach detects and prevents compromised IDS nodes, malicious features injection, manipulation, or deletion, and it is also scalable with low latency.

Nowadays, most of the world's population has become much dependent on computers for banking, healthcare, shopping, and telecommunication. Security has now become a basic norm for computers and its resources since it has become inherently insecure. Security issues like Denial of Service attacks, TCP SYN Flooding attacks, Packet Dropping attacks and Distributed Denial of Service attacks are some of the methods by which unauthorized users make the resource unavailable to authorized users. There are several security mechanisms like Intrusion Detection System, Anomaly detection and Trust model by which we can be able to identify and counter the abuse of computer resources by unauthorized users. This paper presents a survey of several security mechanisms which have been implemented using Fuzzy logic. Fuzzy logic is one of the rapidly developing technologies, which is used in a sophisticated control system. Fuzzy logic deals with the degree of truth rather than the Boolean logic, which carries the values of either true or false. So instead of providing only two values, we will be able to define intermediate values.

In this digital era, technology is upgrading day by day and becoming more agile and intelligent. Smart devices and gadgets are now being used to find solutions to complex problems in various domains such as health care, industries, entertainment, education, etc. The Transport system, which is the biggest challenge for any governing authority of a state, is also not untouched with this development. There are numerous challenges and issues with the existing transport system, which can be addressed by developing intelligent and autonomous vehicles. The existing vehicles can be upgraded to use sensors and the latest communication techniques. The advancements in the Internet of Things (IoT) have the potential to completely transform the existing transport system to a more advanced and intelligent transport system that is the Internet of Vehicles (IoV). Due to the connectivity with the Internet, the Internet of Vehicles (IoV) is exposed to various security threats. Security is the primary issue, which requires to be addressed for success and adoption of the IoV. In this paper, the applicability of machine learning based solutions to address the security issue of IoV is analyzed. The performance of six machine-learning algorithms to detect Bot threats is validated by the k-fold cross-validation method in python.

The computer network is used by billions of people worldwide for variety of purposes. This has made the security increasingly important in networks. It is essential to use Intrusion Detection Systems (IDS) and devices whose main function is to detect anomalies in networks. Mostly all the intrusion detection approaches focuses on the issues of boosting techniques since results are inaccurate and results in lengthy detection process. The major pitfall in network based intrusion detection is the wide-ranging volume of data gathered from the network. In this paper, we put forward a hybrid anomaly based intrusion detection system which uses Classification and Boosting technique. The Paper is organized in such a way it compares the performance three different Classifiers along with boosting. Boosting process maximizes classification accuracy. Results of proposed scheme will analyzed over different datasets like Intrusion Detection Kaggle Dataset and NSL KDD. Out of vast analysis it is found Random tree provides best average Accuracy rate of around 99.98%, Detection rate of 98.79% and a minimum False Alarm rate.

An IDS is a system that helps in detecting any kind of doubtful activity on a computer network. It is capable of identifying suspicious activities at both the levels i.e. locally at the system level and in transit at the network level. Since, the system does not have its own dataset as a result it is inefficient in identifying unknown attacks. In order to overcome this inefficiency, we make use of ML. ML assists in analysing and categorizing attacks on diverse datasets. In this study, the efficacy of eight machine learning algorithms based on KDD CUP99 is assessed. Based on our implementation and analysis, amongst the eight Algorithms considered here, Support Vector Machine (SVM), Random Forest (RF) and Decision Tree (DT) have the highest testing accuracy of which got SVM does have the highest accuracy

As wireless mesh networks (WMNs) develop rapidly, security issue becomes increasingly important. Intrusion Detection System (IDS) is one of the crucial ways to detect attacks. However, IDS in wireless networks including WMNs brings high detection overhead, which degrades network performance. In this paper, we apply compressed sensing (CS) theory to IDS and propose a CS based IDS for hybrid WMNs. Since CS can reconstruct a sparse signal with compressive sampling, we process the detected data and construct sparse original signals. Through reconstruction algorithm, the compressive sampled data can be reconstructed and used for detecting intrusions, which reduces the detection overhead. We also propose Active State Metric (ASM) as an attack metric for recognizing attacks, which measures the activity in PHY layer and energy consumption of each node. Through intensive simulations, the results show that under 50% attack density, our proposed IDS can ensure 95% detection rate while reducing about 40% detection overhead on average.

This paper presents the effects of problem based learning project on a high-school student in Technology school “Electronic systems” associated with Technical University Sofia. The problem is creating an intrusion detection system for Apache HTTP Server with duration 6 months. The intrusion detection system is based on a recurrent neural network classifier namely long-short term memory units.

This paper presents a contextual anomaly detection method and its use in the discovery of malicious voltage control actions in the low voltage distribution grid. The model-based anomaly detection uses an artificial neural network model to identify a distributed energy resource's behaviour under control. An intrusion detection system observes distributed energy resource's behaviour, control actions and the power system impact, and is tested together with an ongoing voltage control attack in a co-simulation set-up. The simulation results obtained with a real photovoltaic rooftop power plant data show that the contextual anomaly detection performs on average 55% better in the control detection and over 56% better in the malicious control detection over the point anomaly detection.

We propose a new security paradigm that makes cross-layer personalization a premier component in the design of security solutions for computer infrastructure and situational awareness. This paradigm is based on the observation that computer systems have a personalized usage profile that depends on the user and his activities. Further, it spans the various layers of abstraction that make up a computer system, as if the user embedded his own DNA into the computer system. To realize such a paradigm, we discuss the design of a comprehensive and cross-layer profiling approach, which can be adopted to boost the effectiveness of various security solutions, e.g., malware detection, insider attacker prevention and continuous authentication. The current state-of-the-art in computer infrastructure defense solutions focuses on one layer of operation with deployments coming in a "one size fits all" format, without taking into account the unique way people use their computers. The key novelty of our proposal is the cross-layer personalization, where we derive the distinguishable behaviors from the intelligence of three layers of abstraction. First, we combine intelligence from: a) the user layer, (e.g., mouse click patterns); b) the operating system layer; c) the network layer. Second, we develop cross-layer personalized profiles for system usage. We will limit our scope to companies and organizations, where computers are used in a more routine and one-on-one style, before we expand our research to personally owned computers. Our preliminary results show that just the time accesses in user web logs are already sufficient to distinguish users from each other,with users of the same demographics showing similarities in their profiles. Our goal is to challenge today's paradigm for anomaly detection that seems to follow a monoculture and treat each layer in isolation. We also discuss deployment, performance overhead, and privacy issues raised by our paradigm.